Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep Learning

Amino acid mutations may lead to significant changes in the binding affinity of protein complexes, thereby causing a series of cellular dysfunctions. Therefore, accurate prediction of protein-protein binding affinity changes (ΔΔG) induced by amino acid mutations is of great importance for understanding protein-protein interactions (PPIs). In this study, we propose SAMAffinity, a protein sequence-aware deep learning architecture for predicting changes in protein-protein binding affinity caused by amino acid mutations. SAMAffinity predicts mutation-induced ΔΔG by integrating multi-source sequence features, leveraging a Mutation-Site Identification (MSI) module to highlight local semantic shifts and a Binding-Interface Awareness (BIA) module to capture interaction changes. Benchmark evaluations on public datasets show that under the mutation-level data splitting strategy, SAMAffinity outperforms the state-of-the-art sequence-based method AttABseq by 33.3%, 72.3%, 31.8%, and 30.5% on S1131, S4169, S645, and M1101 datasets, respectively. Moreover, under the complex-level data splitting strategy, SAMAffinity surpasses the structure-based method MpbPPI by 22.9%, 22.7%, 5.0%, and 11.4% on the corresponding datasets. Beyond predictive accuracy, the strong consistency between the model’s predicted distribution and natural amino-acid mutation tendencies indicates that SAMAffinity effectively captures the underlying mutational landscape shaped by intrinsic biochemical and evolutionary factors. Based on this capability, SAMAffinity demonstrated strong generalization in a study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, suggesting its potential for optimizing therapeutic antibody design.